An ideal choice for network-based parameter inference can be found in graph-based representation learning algorithms like LINE [1]}, DeepWalk [2]}, and node2vec [3]}. Based on the “you shall be known by the company you keep” principle frequently cited in text-based representation learning systems like word2vec [4]}, the goal of graph representation learning systems is to learn a vector-representation (called an embedding) of each node based on the embeddings of the node's neighborhood.
